System and method to enable the application of optical tracking techniques for generating dynamic quantities of interest with alias protection

ABSTRACT

Systems and methods for realizing practical applications of high speed digital image correlation (DIC) for dynamic quantities of interest are provided. In particular, a series of images are captured for a component of interest in which a non-filtered sensor and an analog low-pass filtered sensor are included within the region of interest for the series of images. Displacement signals are obtained for the component of interest, the non-filtered sensor, and the analog low-pass filtered sensor by applying digital image correlation processing to the series of images, which may also be wavelet filtered. Dynamic quantities of interest may be generated and derived from the displacement signals after having been wavelet filtered. Such dynamic quantities of interest based on the wavelet filtered DIC-derived displacement signal may be compared to sensor-derived dynamic quantities of interest to determine if aliasing is or is likely to be present.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/809,563, filed Jul. 27, 2015, the entire disclosure of which ishereby incorporated herein by reference for all that it teaches and forall purposes.

FIELD OF THE DISCLOSURE

The disclosure relates to a system and method for realizing practicalapplications of high speed digital image correlation for dynamicquantities of interest, where dynamic quantities of interest may includeShock Response Spectrum (SRS), Pseudo Velocity Shock Response Spectrum,velocity/acceleration time history, and harmonic wavelet map, forexample.

BACKGROUND

Digital image correlation (DIC) is a non-contact measurement techniquethat uses high-resolution machine-vision digital cameras to accuratelymeasure surface deformation in two or three dimensions. The field ofhigh speed DIC, in addition to other methods of optical tracking, isadvancing due to improvements in optical sensor sensitivity, imageprocessing, and computer processor speed. However, even taking intoaccount such advancements, improvements with respect to filtering,analysis, and use of data derived from DIC techniques are needed toavoid invalid conclusions and recommendations based on erroneouslycaptured and filtered data.

SUMMARY

Theoretical measurement of DIC-derived dynamic quantities of interest,such as Shock Response Spectrum (SRS), Pseudo Velocity Shock ResponseSpectrum, velocity/acceleration time history, harmonic wavelet map,etc., include a DIC-based calculation requiring temporaldifferentiation. However, reliable DIC-derived dynamic quantities ofinterest are impractical for a variety of reasons including, but notlimited to, the difficulty associated with differentiating real DICdisplacement signals in order to obtain dynamic quantities of interestas well as the inability to guard against temporal aliasing due to thefinite exposure limitations of digital image acquisition systems. Thatis, comparative approaches to temporal aliasing of DIC data productsinvolve oversampling, random sampling, and implementing digital low-passfiltering techniques. Such an approach has been demonstrated, however,to have practical limitations due in part to the frequency-rich signalsoften encountered and the Analog/Digital conversion limitations thatmake it impossible to digitally filter data after the aliased sample hasbeen taken. Further evidence of the need for temporal analoganti-aliasing filters is practically demonstrated by the fact thatmodern Data Acquisition Systems (DAS) come equipped with analoganti-aliasing low-pass filters to guard against the temporal aliasingphenomena. However, no known low-pass temporal optical analoganti-aliasing filter exists that can be used to protect DIC fromaliasing.

Further, when differentiating DIC signals, such as displacementinformation generated from DIC techniques, to obtain dynamic quantitiesof interest, errors due in part to noise or uncertainly are severelyamplified. Accordingly, computing temporal derivatives from raw datausing the central difference method for example, amplifies errors due tonoise or uncertainly at each differentiation step making the dynamicquantities of interest unreliable. Therefore, comparative approachesusing DIC-derived data products that require temporal numericaldifferentiations typically use Fourier filtering techniques that assumeperiod signals. However, periodicity tends to be a poor assumption formost high speed DIC applications because of the inherent non-periodicnature of phenomena that merit DIC application in the first place.Further still, high speed camera memory limits also contribute toimpractical Fourier filtering applicability. Similarly, comparativeapproaches using DIC-derived data products tend to overemphasize thequalitative aspects of software calculated derivatives providing limitedquantitative use. Therefore, ignorance of the aliasing phenomena inaddition to the use of periodic filtering techniques and an overrelianceon software calculated derivatives may lead to invalid conclusions andrecommendations based on corrupt data.

In accordance with aspects of the present disclosure, a system forgenerating dynamic quantities of interest with alias protection based ondigital image optical tracking techniques is provided. Morespecifically, embodiments of the present disclosure address temporalaliasing and differentiating of high speed DIC data for dynamicquantities of interest (Shock Response Spectrum (SRS), Pseudo VelocityShock Response Spectrum, velocity/acceleration time history, harmonicwavelet map, custom, etc.) by introducing two sensors, such asaccelerometers, into the Region of Interest (ROI) of a DIC system. Thatis, the ROI of a DIC system includes a first sensor that is analoglow-pass filtered and a second sensor that is not analog low-passfiltered, where the sensor that is not analog low-pass filtered is alsoreferred to herein as a non-filtered sensor. Additionally, instead ofapplying Fourier filters to DIC displacement signals, embodiments of thepresent disclosure utilize one or more wavelet de-noising filters,together with an appropriate wavelet decomposition level, to filteroptically displacement signals. Accordingly, the DIC displacement signalmay be differentiated and used to calculate a dynamic quantity ofinterest at both sensor locations. The resulting optically-deriveddynamic quantity of interest at each sensor location may then becompared to the associated sensor-derived calculation of the dynamicquantity of interest. In addition, the wavelet filter type and levelcombination may be determined based on a similarity algorithm, such asSum of Absolute Differences (SAD), Sum of Squared Differences (SSD),etc. Such a similarity algorithm may be specified by a user or may bethe result of an algorithm determination process. In accordance withembodiments of the present disclosure, temporal high speed DICanti-aliasing protection is achieved by comparing the two collocatedsensors for aliasing and/or determining whether the optically-deriveddynamic quantity of interest best resembles the filtered or unfiltereddynamic quantity of interest measure.

In accordance with embodiments of the present disclosure, a system isprovided, the system including at least one camera focused on a regionof interest, a sensor located within the region of interest, and a dataprocessing system adapted to receive sensor data from the sensor and aseries of images, each image including the region of interest, from theat least one camera. In embodiments, the data processing system includesat least one processor and memory storing one or more programinstructions that when executed by the at least one processor, executethe steps of: generating an optically-derived dynamic quantity ofinterest for the sensor based on the series of images provided from theat least one camera, generating a dynamic quantity of interest based onsensor data provided from the sensor, comparing the optically-deriveddynamic quantity of interest for the sensor to the dynamic quantity ofinterest based on sensor data provided from the sensor, and generatingan indication of aliasing based on a measure of similarity between theoptically-derived dynamic quantity of interest for the sensor and thedynamic quantity of interest based on sensor data provided from thesensor.

In accordance with another embodiment of the present disclosure, amethod is provided, the method comprising: generating a first dynamicquantity of interest for a sensor based on a series of images providedfrom at least one camera directed at a region of interest; generating asecond dynamic quantity of interest based on sensor data provided fromthe sensor; comparing the first dynamic quantity of interest to thesecond dynamic quantity of interest; and generating an indication ofaliasing based on a measure of similarity between the first dynamicquantity of interest and the second dynamic quantity of interest.

Further still, in accordance with embodiments of the present disclosure,a system is provided, the system including at least one camera focusedon a region of interest, a first sensor, a second sensor, and a dataprocessing system adapted to receive sensor data and the series ofimages, wherein the sensor data is from the first sensor and the secondsensor and each image includes the region of interest. In embodiments,the data processing system includes at least one processor and memorystoring one or more program instructions that when executed by the atleast one processor, execute the steps of: determining a first dynamicquantity of interest for the first sensor based on a displacement signalderived from the series of images via a digital image correlationtechnique, wherein the displacement signal is wavelet filtered accordingto at least one of a wavelet filter type and a wavelet decompositionlevel, determining a second dynamic quantity of interest for the secondsensor based on a displacement signal derived from the series of imagesvia the digital image correlation technique, wherein the displacementsignal is wavelet-filtered according to at least one of a wavelet filtertype and a wavelet decomposition level, determining a third dynamicquantity of interest for the first sensor based on analog low-passfiltered sensor data provided by the first sensor, determining a fourthdynamic quantity of interest for the second sensor based on non-filteredsensor data provided by the second sensor, comparing the first dynamicquantity of interest to the third dynamic quantity of interest,comparing the second dynamic quantity of interest to the fourth dynamicquantity of interest, generating an indication of aliasing based on oneor more of a measure of similarity between the first dynamic quantity ofinterest and the third dynamic quantity of interest and a measure ofsimilarity between the second dynamic quantity of interest and thefourth dynamic quantity of interest, and determining a fifth dynamicquantity of interest for a component of interest based on awavelet-filtered displacement signal derived from the series of imagesvia the digital image correlation technique.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present disclosure are described inconjunction with the appended figures wherein:

FIG. 1 depicts a system for realizing practical applications of highspeed DIC for dynamic quantities of interest with alias protection inaccordance with at least some embodiments of the present disclosure;

FIG. 2 is a block diagram of a data processing system in accordance withan exemplary embodiment of the present disclosure;

FIG. 3 is a block diagram depicting details of a camera and a dataacquisition system in accordance with an exemplary embodiment of thepresent disclosure;

FIG. 4 depicts a schematic overview of an example correlation process inaccordance with embodiments of the present disclosure;

FIG. 5 illustrates a chart of an optically-derived displacement signalover time in accordance with embodiments of the present disclosure;

FIGS. 6A-6B depict a flow diagram of a first method for providingfiltering and aliasing protection for optically-derived dynamicquantities of interest in accordance with embodiments of the presentdisclosure;

FIGS. 7A-B depict a flow diagram of a second method for providingfiltering and aliasing protection for optically-derived dynamicquantities of interest in accordance with embodiments of the presentdisclosure;

FIG. 8 illustrates a first chart depicting a raw and filteredoptically-derived displacement signal over time in accordance withembodiments of the present disclosure;

FIG. 9 illustrates a first chart depicting a raw and filteredoptically-derived dynamic quantity of interest together with anaccelerometer derived dynamic quantity of interest in accordance withembodiments of the present disclosure;

FIG. 10 illustrates a chart depicting saturated and correctedaccelerometer data in accordance with embodiments of the presentdisclosure;

FIG. 11 illustrates a second chart depicting a raw and filteredoptically-derived displacement signal over time in accordance withembodiments of the present disclosure; and

FIG. 12 illustrates a second chart depicting raw and filteredoptically-derived dynamic quantity of interest together with anaccelerometer derived dynamic quantity of interest in accordance withembodiments of the present disclosure.

DETAILED DESCRIPTION

The ensuing description provides embodiments only, and is not intendedto limit the scope, applicability, or configuration of the claims.Rather, the ensuing description will provide those skilled in the artwith an enabling description for implementing the embodiments. It beingunderstood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope ofthe appended claims.

Referring initially to FIG. 1, details of a system 100 for realizing thepractical applications of high speed DIC for dynamic quantities ofinterest with alias protection are depicted in accordance with at leastsome embodiments of the present disclosure. The system 100 generallyincludes a test fixture 104, one or more cameras 108A-B that obtain aseries of images 112 at a desired frame rate, at least one dataacquisition system 116A-B, and at least one data processing system 120.Sensor data, such as acceleration data, may be obtained from at leasttwo sensors located within the field of view of the one or more cameras108A-B. Further, the series of images 112 captured by the one or morecameras 108A-B may be used to derive displacement, velocity, andacceleration data for components within the field of view of the cameras108A-B, and more specifically, within the region of interest 132. Thedata provided by the sensors may be used in combination with theoptically-derived displacement, velocity, acceleration, and/or othermeasures for one or more components within the field of view of the oneor more cameras 108A-B to determine the presence, or lack of, aliasing.

The test fixture 104 illustrated in FIG. 1 may be one of many testfixtures utilized in accordance with embodiments of the presentdisclosure. That is, although specific details of the test fixture 104are provided herein, such details should not be considered limiting asother test fixtures, equipment, and techniques exist for realizing oneor more applications of high speed DIC for generating dynamic quantitiesof interest. As one example, test fixtures capable of supportingdropping mass, air gun, and/or explosive excitations are contemplated.Further, test fixtures capable of supporting one or more vibrationanalyses are contemplated as well.

With further reference to FIG. 1, the test fixture 104 may include atleast one component of interest 124, at least one excitation device 128,at least one non-filtered sensor 144, and at least one analog low-passfiltered sensor 148. In accordance with embodiments of the presentdisclosure, the systems and methods disclosed herein are particularlyapplicable to shock and vibration testing of components within theaerospace industry, geophysical drilling industry, and any other areaswhere shock and vibration, and, more particularly, shock and/orvibration of a non-periodic nature, is present. Due in part to cameramemory limitations and a corresponding rate of acquiring images,techniques described herein may be equally applicable to periodicsignals for the same or similar reasons as non-periodic signals becausethe periodic signal does not have time to complete multiple cycles.

The component of interest 124, also referred to as an object under test,may generally include any object undergoing testing for which dynamicquantities of interest are of concern. Examples of the component ofinterest 124 include, but are not limited to, printed circuit boards(PCBs) and other electronic components, aerospace components, such as aclamp band opening device, and geophysical drilling components, such asdrill bits, rotary steerable systems, drill collars, and drillpipe. Insome instances, the component of interest 124 may be the region ofinterest 132. As one non-limiting example, and in accordance withembodiments of the present disclosure, dynamic quantities of interestrelated to wave propagation of a plate may be of interest. In suchinstances, the component of interest 124 would be the same as the regionof interest 132.

The excitation source device 128 may include, but is not limited to,explosive, air gun, mass drops, impact drops, and other means ofachieving a desired shock and/or vibration. An explosive excitationsource device, such as a pyroshock, also known as pyrotechnic shock,generally includes contact and non-contact explosions, where a contactexplosion may occur on the same test fixture 104 on which the componentof interest 124 is mounted, and a non-contact explosion may occur on adifferent test fixture from which the component of interest 124 ismounted. The magnitude and shape of the resulting pyroshock maygenerally be controlled by using a specified test fixture 104, modifyingthe test fixture 104, utilizing a contact or non-contact explosion,altering the location and size of the explosive charge, altering how theexplosive charge and/or the component of interest 124 are mounted to thetest fixture 104, and altering the location of the component of interest124 in relation to the test fixture and/or the explosive excitationsource device 128. With respect to mass drops, also known as a droppingmass, a mass of any shape and size may be dropped from a given heightand may impact the test fixture 104 directly or a plate or otherdevice/piece attached to the test fixture 104. Similar to the explosiveexcitation source device 128, the magnitude and shape of the resultingshock may generally be controlled by using a specified test fixture 104,modifying the test fixture 104, changing a direction of impact, alteringthe location and means for mounting the component of interest 124, andaltering properties/characteristics of the mass, such as, but notlimited to, the hitting tip material, shape, and size, the weight of themass, and the height at which the mass is dropped. Further, the locationand/or area of impact may also be altered to achieve a desired shock ofa specified magnitude.

Similar to the dropping mass, a shock generated from an air gun usuallyresults from an object, such as a missile, being fired from the air gunand impacting a specified location on or attached to the test fixture104. To control the magnitude and shape of the resulting shock, the testfixture 104 may be modified or chosen specifically for a specifiedshock, the impact location and material may be altered, the impactdirection may be altered, and/or the characteristics/parameters of themissile, such as, but not limited to, weight, material, and speed, maybe altered. Although the explosive, air gun, and dropping mass werediscussed in detail as pertaining to the excitation source device 128,embodiments of the present disclosure are applicable to various otherexcitation source devices 128 and combinations thereof.

In accordance with embodiments of the present disclosure, the testfixture 104 includes one or more non-filtered sensors 144 and one ormore analog low-pass filtered sensors 148 that provide sensor dataresulting from the excitation source device 128, the component ofinterest 124, and/or the testing environment to a data acquisitionsystem 116A-B. Each of the sensors 144 and 148 may be selected accordingto a desired sensitivity, measurement range, frequency range, andresonant frequency. The non-filtered sensor 144 is utilized to obtainraw sensor data resulting from excitation source device 128, thecomponent of interest 124, and/or the testing environment. Such rawsensor data is not subjected to an anti-aliasing filter, also referredto as an analog low-pass filter, and is utilized in accordance withembodiments of the present disclosure to provide aliasing protection foroptically-derived dynamic quantities of interest.

The analog low-pass filtered sensor 148 is utilized to obtain filteredacceleration data resulting from excitation source device 128, thecomponent of interest 124, and/or the testing environment. The cutofffrequency of a filter for the analog low-pass filtered sensor 148 may beselected and/or may be adjusted based on a predetermined and/or selectedcriteria in conjunction with the data acquisition system 116A-B in orderto prevent aliasing issues arising from high-amplitude andhigh-frequency sources of energy, such as those sources modeled by shockand pyrotechnic shock pulses. When analog anti-aliasing filters, such asa low-pass filter having a specified cutoff frequency, are not utilized,high frequency shock energy may be “folded down” about the Nyquistfrequency thereby artificially increasing the apparent shock energy orlevel at a lower frequency, which may lead to invalid conclusions andrecommendations based on such misleading data. Accordingly, the dataacquisition system 116A-B as a whole, including the analog low-passfiltered sensor 148, but excluding the non-analog low-pass filteredsensor 144, are configured to minimize the effects of aliasing andensure that the digitized data resulting from the analog-to-digitalconverter of the data acquisition system 116A-B is correct. Such ananalog low-pass filter may be included in the analog low-pass filteredsensor 148, as part of a signal conditioner component within or outsideof the data acquisition system 116A-B, and/or combinations thereof.

In accordance with embodiments of the present disclosure, the cutofffrequency of the analog low-pass filtered sensor 148 may be selected tosatisfy Shannon's Sample Theorem, which states that a sampled timesignal must not contain components at frequencies above the Nyquistfrequency. The Nyquist frequency is one-half of the sampling frequency,where the minimum sampling frequency is chosen to be at least twice themaximum frequency component of a source signal in order to satisfy theNyquist Sampling Theorem. Therefore, in order to reduce and/or eliminatecomponents at frequencies above the Nyquist frequency, the cutofffrequency of the analog low-pass filtered sensor 148 is generally setat, or slightly above, the maximum analysis frequency.

However, in configuring and testing various components of interest 124,a maximum analysis frequency may be unknown. That is, when consideringstage separation and other examples from the launch vehicle industry,shock events impacting a drill collar occurring during a drillingoperation, and/or other applications involving non-periodic andnon-repeatable shock-type events, the maximum expected frequency fromthe source of the shock energy is essentially unknown; accordingly, thesampling rate may be set to an exceedingly high value in order tocapture such high frequency signal components. Thus, the cutofffrequency of the low-pass filter for the analog low-pass filtered sensor148 may be set at or slightly above one-half of the exceedingly highsampling rate value. Alternatively, or in addition, the maximum analysisfrequency and/or the sampling rate may be configured in accordance withindustry guidelines. That is, industry guidelines suggest that thesampling rate be at least ten times greater than the maximum analysisfrequency. Accordingly, the cutoff frequency of the low-pass filter forthe analog low-pass filtered sensor 148 may be set to one-tenth of thesampling rate. Of course, it should be understood that the cutoff rateof the analog low-pass filter may impact the configuration of the cutofffrequency; accordingly, the cutoff rate of the low-pass filter should beconsidered when configuring or specifying a cutoff frequency.

In accordance with embodiments of the present disclosure, the one ormore cameras 108A-B acquire a series of images 112 at a desired framerate and provide the series of images 112 to a data acquisition system116A-B. The data acquisition system 116A-B may be part of an overalldata acquisition system, may be one or more separate standalone dataacquisition systems, or may be combinations thereof. For example, thedata acquisition system 116B may include one or more high speed videoacquisition cards specifically configured to obtain a series of images112 at a specified frame rate, or sampling rate, and resolution. Aspreviously discussed, the sample rate may be configured based on anoverall data acquisition system configuration and may be determinedbased on an expected maximum analysis frequency. However, when a maximumanalysis frequency is unknown, the frame rate may be set to anexceedingly high value in accordance with the capabilities andlimitations of the data acquisition system 116B and the data processingsystem 120. Accordingly, as the series of images 112 are not subjectedto temporal aliasing protection at the time of acquisition, the dataacquired from and/or derived based on the series of images 112 may bemisleading due in part to the effects of aliasing.

Thus, in accordance with embodiments of the present disclosure, acomparison can be made between the data acquired from the non-filteredsensor 144 and the analog low-pass filtered sensor 148 to determine ifaliasing at the non-analog low-pass filtered sensor 144 is contributingto or otherwise causing erroneous results in the data from the analoglow-pass filtered sensor 148 and/or the DIC-derived quantities ofinterest based on the series of images 112. As will be discussed below,such a comparison may be based on an agreement, similarity, and/orcorrelation between the data acquired from two sources. For example, ifan SRS plot of data acquired from the non-filtered sensor 144, such as anon-filtered accelerometer, and the analog low-pass filtered sensor 148,such as an analog low-pass filtered accelerometer, demonstrates that thenon-filtered sensor 144 deviates from or is unreasonably higher than thedata acquired from the analog low-pass filtered sensor 148, aliasing maybe the reason for the discrepancy. Further, if the correlation oragreement between an SRS plot of data acquired from the non-filteredsensor 144 and the analog low-pass filtered sensor 148 is above or belowa predetermined or selected threshold, aliasing may be the reason forthe deviation. Therefore, if aliasing is detected based on a comparisonbetween the data acquired from the non-filtered sensor 144 and theanalog low-pass filtered sensor 148, aliasing may also exist orotherwise influence data acquired from the series of images 112. Thatis, if aliasing is detected based on a comparison between the dataacquired from the non-filtered sensor 144 and the analog low-passfiltered sensor 148, aliasing may also exist or otherwise influenceDIC-derived quantities of interest, such as displacement, velocity, andacceleration data, based on the series of images 112 acquired from theone or more cameras 108A-B. In such instances, additional filteringtechniques may be applied to the data acquired from and/or derived basedon the series of images 112 to remove effects due to aliasing.Alternatively, or in addition, such data acquired from and/or derivedfrom the one or more cameras 108A-B may be tagged, or otherwiseidentified, and excluded from further consideration.

Included on the test fixture 104, the component of interest 124, theshock source device 128, non-filtered sensor 144, and/or the analoglow-pass filtered sensor 148, is a target pattern tracking pattern/mark140, and in some instances, a more specific speckle pattern 136. Thatis, since digital image correlation is an optical, non-contact method ofmeasuring surface deformation in two or three dimensions, the process ofacquiring displacement signals illustrative of surface deformation, aswill be discussed further below, relies on tracking the displacement ofa unique surface characteristics through a series of images 112. Suchunique surface characteristics may comprise speckle pattern 136 and/or atracking pattern/mark 140. The movement, or displacement, of a localizedspeckle pattern 136 and/or the tracking pattern/mark 140 on a componentof interest 124, and in accordance with embodiments of the presentdisclosure—the non-filtered sensor 144 and the analog low-pass filteredsensor 148—is utilized to generate a displacement signal for thecomponent. An example displacement signal over time is illustrated inFIG. 5. Such a displacement signal may be differentiated to calculate avelocity or other dynamic quantity of interest component. Further, thevelocity may be differentiated or subject to further calculation togenerate another dynamic quantity of interest component, such asacceleration.

In accordance with embodiments of the present disclosure, at least onedisplacement signal is generated for the component of interest 124, thenon-filtered sensor 144, and the analog low-pass filtered sensor 148.Based on such displacement signals and further based on subsequentderived filtering characteristics and aliasing analyses, otherdisplacement signals generated for components within the region ofinterest 132 may be filtered based on the previously derived filteringcharacteristic. Further, should the aliasing analyses indicate that thedisplacement signal for the component of interest 124 and/or theDIC-derived quantities of interest for the component of interest 124 arealiased, the same or similar technique for correcting and/or taggingaliased information for the other displacement signals generated forcomponents within the region of interest 132 may be utilized.

In accordance with some embodiments of the present disclosure, sensors144 and/or sensor 148 may be located outside the region of interest 132,for example as sensor 146 on the back of the test apparatus 104.Locating the sensors outside the region of interest 132 allows for morecomponents of interest to be located within the finite region ofinterest. For example, adding more components within the region ofinterest is of high value when applied to costly pyrotechnic shocktesting.

To enable such an implementation, the ability to extrapolate thecorresponding optical measurements within the region of interest 132 tothe sensor locations outside the region of interest 132 with certaintyappropriate to meet the intent of the application of the proposed methodis required. For example, one or both of the at least two sensors 144and 148 may be located outside the region of interest 132, such as onthe back of the test apparatus 104 as sensor 146, provided the opticalmeasurements used for comparison within the region of interest 132 maybe extrapolated with some degree of certainty. As one example, thinplate responses across the thickness of a plate can be practicallyneglected over a range of response frequencies. A sensor of knownlocation, relative to an optical measurement within the region ofinterest, on the back of a region of interest for a plate can still beimplemented in accordance with embodiments of the present disclosure.If, however, thickness cannot be neglected but corrections are knownwith adequate detail and certainty, the sensor may still be located onthe back of a region of interest 132.

As another example, considering a point along a cantilevered beam inpure transverse motion at first mode resonance, if the region ofinterest 132 is on the top of the beam, sensors may be located at aknown location relative to the top region of interest on the non-visiblebottom half of the beam because the sensor response may be correctedwith simple beam theory. For instance, simple beam theory predicts topmembers of a cantilever beam to be in tension while bottom members arecompressed. Thus, a sensor response would simply be in the oppositedirection as the optically-derived measurements in this case. By simplyflipping a sensor sign response for this case, embodiments describedherein may be implemented provided beam theory, which neglects effects,such as transverse shear, meets the intent of the application.

It is important to note that any combination of analog sensors whosemeasurement can be correlated with optical tracking measurement systemsproducts can be used to provide alias protection. Accordingly, examplesof sensors 144, 146, and 148 include, but are not limited to, an analogstrain gauge, an analog accelerometer, a linear variable displacementtransformer (LVDT), and/or combinations thereof. For example, an analogstrain gauge and analog anti-aliased accelerometer or analoganti-aliased strain gauge and analog accelerometer might be used as thesensors 144 and 148. Accordingly, any combination of analog and analoganti-aliased sensors may be used depending on a testing intent.

FIG. 2 depicts additional details with respect to a data processingsystem in accordance with embodiments of the present disclosure. Moreparticularly, the data processing system 120 may generally include aprocessor 204, memory 208, user input 212, user output 216, storage 220,a communication interface 232, a dynamic quantities of interest analysismodule 240, and, in some instances, a data acquisition module 236.Processor 204 is provided to execute instructions contained withinmemory 208. Accordingly, the processor 204 may be implemented as anysuitable type of microprocessor or similar type of processing chip, suchas any general-purpose programmable processor, digital signal processor(DSP) or controller for executing application programming containedwithin memory 208. Alternatively, or in addition, the processor 204 andmemory 208 may be replaced or augmented with an application specificintegrated circuit (ASIC), a programmable logic device (PLD), or a fieldprogrammable gate array (FPGA).

The memory 208 generally comprises software routines facilitating, inoperation, pre-determined functionality of the data processing device120. The memory 208 may be implemented using various types of electronicmemory generally including at least one array of non-volatile memorycells (e.g., Erasable Programmable Read Only Memory (EPROM) cells orFLASH memory cells, etc.). The memory 208 may also include at least onearray of dynamic random access memory (DRAM) cells. The content of theDRAM cells may be pre-programmed and write-protected thereafter, whereasother portions of the memory 208 may be selectively modified or erased.The memory 208 may be used for either permanent data storage ortemporary data storage. Alternatively, or in addition, data storage 220may be provided. The data storage 220 may generally include storage forprograms and data 224, storage for one or more modules included in thedynamic quantities of interest analysis module 240, and storage for adatabase 228. The database 228 may store data associated with thedynamic quantities of interest analysis module 240 and/or the dataacquisition module 236. The communication interface 232 may allow thedata processing device 120 to communicate over a communication networkand/or communicate with one or more data acquisition systems 116A-Band/or directly with the one or more cameras 108A-B. Further, the dataprocessing system 120 may include a portion of, or an entirety of, adata acquisition system 116 within the data acquisition module 236. Forexample, the data acquisition module 236 may include the same or similarfunctionality and capability as one or more of the data acquisitionsystems 116A-B as previously discussed.

The applications of generating dynamic quantities of interest with aliasprotection based on digital image optical tracking techniques may be atleast partially provided by the dynamic quantities of interest analysismodule 240. That is, the optical tracking/displacement module 244 of thedynamic quantities of interest analysis module 240 may generate one ormore displacement signals based on optically-acquired information, suchas optically-acquired information from the series of images 112. As willbe discussed below, the optical tracking/displacement module 244 mayutilize a variety of techniques to generate one or more displacementsignals for the component of interest 124, the non-filtered sensor 144,the analog low-pass filtered sensor 148, and/or other elements withinthe region of interest 132 based on the series of images 112.

The optical tracking/displacement module 244 may utilize a variety ofmotion tracking methods. Examples of motion tracking methods include,but are not limited to, target motion tracking, feature-based motiontracking, pattern projection tracking, and DIC tracking. Whereas aspecial pattern and/or other target may be affixed to a component ofinterest for DIC tracking and target motion tracking techniques, lessinvasive motion tracking methods, such as feature-based motion trackingand pattern projection tracking may be utilized. For example, patternprojection-based tracking, where a pattern is projected and detected,may be utilized in some instances. However, due to the inherentlimitations in pattern projection technology (e.g., being limited to aspecific frame rate) applications of such tracking techniques may belimited in practice.

The filtering module 248 of the dynamic quantities of interest analysismodule 240 may apply one or more filtering algorithms to remove orotherwise reduce an amount of noise from one or more of the previouslymentioned displacement signals. That is, in accordance with embodimentsof the present disclosure, the filtering module 248 may apply waveletfiltering techniques to reduce or otherwise remove noise from one ormore displacement signals generated by the optical tracking/displacementmodule 244. Alternatively, or in addition, the filtering module 248 mayutilize best fit techniques to adjust a filter type, and, in someinstances, other characteristics of the filter, such as, but not limitedto, a decomposition level and whether to implement thresholding, toachieve a best fit between an optically-derived quantity of interest andan associated sensor-derived dynamic quantity of interest. For example,a wavelet type and a wavelet decomposition level may be selected toachieve a best fit between a DIC-derived quantity of interest based on adisplacement signal generated for the non-filtered sensor 144 and theassociated sensor-derived calculation of the dynamic quantity ofinterest based on the sensor signal, or data, from the non-filteredsensor 144. Similarly, a wavelet type and a wavelet decomposition levelmay be selected to achieve a best fit between a DIC-derived quantity ofinterest based on a displacement signal generated for the analoglow-pass filtered sensor 148 and the associated sensor-derivedcalculation of the dynamic quantity of interest based on the sensorsignal, or data, from the analog low-pass filtered sensor 148. Thewavelet type and the wavelet filter for each of the non-filtered sensor144 and the analog low-pass filtered sensor 148 may be the same ordifferent. Examples of wavelet filters that may be used to de-noise anoptically-derived displacement signal include, but are not limited to,Haar, Daubechies, Symlets, Coiflets, BiorSplines, ReverseBior, Meyr,Dmeyer, Gaussian, Mexian Hat, Morlet, Complex Gaussian, Shannon,Frequency B-Spline, and Complex Morlet.

The aliasing analysis module 252 of the dynamic quantities of interestanalysis module 240 may apply one or more techniques to determine ifaliasing is, or is likely to be, present or otherwise influence one ormore optically-derived (e.g., DIC-derived) dynamic quantities ofinterest. As previously discussed, a comparison can be made between thedata acquired from the non-filtered sensor 144 and the analog low-passfiltered sensor 148 to determine if aliasing at the non-filtered sensor144 is contributing to or otherwise causing erroneous results in thedata acquired from the analog low-pass filtered sensor 148 and/or theoptically-derived dynamic quantities of interest. For example, thealiasing analysis module 252 may determine an amount of correlation, oragreement, between an SRS plot of data acquired from the non-filteredsensor 144, such as an accelerometer, and the analog low-pass filteredsensor 148, such as another accelerometer. The aliasing analysis module252 may also determine if the amount of correlation, or agreement, isabove or below a predetermined or selected threshold indicative ofaliasing. Therefore, if the aliasing analysis module 252 determines thataliasing is likely to be present, the aliasing analysis module 252 maygenerate or otherwise provide an indication of such. For example, thealiasing analysis module 252 may communicate with the graphical userinterface/generator 256 to cause the graphical user interface/generator256 to generate an indication that aliasing may be present. Thegraphical user interface/generator 256 may then provide such indicationto an output device, such as user output 216.

Alternatively, or in addition, the aliasing analysis module 252 maydetermine if one or more of the optically-derived (e.g., DIC-derived)dynamic quantities of interest best resembles the dynamic quantity ofinterest generated from the non-filtered sensor 144 or the analoglow-pass filtered sensor 148. That is, if a correlation value, or bestfit value, for the optically-derived dynamic quantity of interest forthe analog low-pass filtered sensor 148 and the quantity of interestderived from the sensor signal, or data, from the analog low-passfiltered sensor 148 indicates a better match than a correlation value,or best fit value, for the optically-derived dynamic quantity ofinterest for the non-filtered sensor 144 and the quantity of interestderived from the sensor signal, or data, from the non-filtered sensor144, then such an indication may indicate that aliasing may be present.Accordingly, additional filtering techniques may be applied tooptically-derived dynamic quantities of interest, and/or raw data, suchas displacement data, from other components within the region ofinterest 132, such as the component of interest 124 or the region ofinterest 132 itself, to remove effects due to aliasing. Alternatively,or in addition, the optically-derived dynamic quantities of interest,and/or displacement data, from the other components within the region ofinterest 132, such as the component of interest 124, may be tagged, orotherwise identified, and excluded from further consideration.Alternatively, or in addition, since the impacts of aliasing may bespecific to one or more frequencies and/or one or more frequency ranges,the aliasing analysis module 252 may identify such frequencies and/orranges. Such indications and/or results from the aliasing analysismodule 252 may be provided to or otherwise utilized by the graphicaluser interface/generator 256. The various components of the computingsystem 120 may communicate utilizing the bus 260.

In accordance with embodiments of the present disclosure, additionaldetails of the one or more cameras 108A-B and the data acquisitionsystem 116A-B are provided in FIG. 3. The one or more cameras 108A-B mayinclude a lens 304, image acquisition module 308, communicationinterface 324, processor 312, memory 316, storage 320, and a bus 328.The lens 304 may focus light indicative of an image onto a surface suchthat the image acquisition module 308 may convert the light into adigital image. The processor 312 may be the same as or similar to theprocessor/controller 204 previously discussed; accordingly, thedescription of processor 312 has been omitted. The memory 316 generallycomprises software routines facilitating, in operation, pre-determinedfunctionality of the one or more cameras 108A-B and may be used foreither permanent data storage or temporary data storage. The memory 316may be the same as or similar to the memory 208; accordingly, thedescription of the memory 316 has been omitted. The data storage 320 maygenerally include storage for programs and data, storage for one or moremodules included in the one or more cameras 108A-B, and storage for aseries of images 112. That is, the storage 320 may acquire a series ofimages 112 and provide the series of images 112 to the data processingsystem 120 via the communication interface 324 and/or the dataacquisition system 116A-B. In some instances, the cameras 108A-B mayobtain the series of images 112 during a test; such series of images 112may be stored within the memory 316 and/or storage 320 such thatbandwidth limitations of the data acquisition system as well as thecables connecting the cameras 108A-B to either the data acquisitionsystem 116A-B or the data processing system 120 do not affect the imageacquisition process. Accordingly, acquired series of images 112 may besubsequently transferred to and analyzed at the data processing system120. Various components of the one or more cameras 108A-B maycommunicate utilizing the bus 328.

In accordance with embodiments of the present disclosure, the dataacquisition system 116A-B may include an interface 332 for receiving oneor more signals. The interface 332 may receive signals from thenon-filtered sensor 144 and the analog low-pass filtered sensor 148. Thesignal from the analog low-pass filtered sensor 148 may be passed to thesignal conditioning module 336. The signal conditioning module 336 mayapply a low-pass filter with a cutoff frequency and cutoff rate aspreviously discussed. The signal from the non-filtered sensor 144 andthe analog low-pass filtered sensor 148 may then be supplied to the dataacquisition module 340. The data acquisition module 340 may include aprocessor 352, memory 348, and an analog/digital converter 344. Theprocessor 352 may be the same as or similar to the processor/controller204 previously discussed; accordingly, the description of processor 352has been omitted. The memory 348 generally comprises software routinesfacilitating, in operation, pre-determined functionality of the dataacquisition system 116A-B and may be used for either permanent datastorage or temporary data storage. The memory 348 may be the same as orsimilar to the memory 208; accordingly, the description of the memory348 has been omitted. In operation, the analog/digital converter 344,together with the processor 352 and the memory 348, may sample one ormore signals from the interface 332 and convert the signals into digitalform. The digitized signals may be stored temporality in the memory 348before being passed to the data processing system 120 via acommunication interface 356. Alternatively, or in addition, the dataacquisition system 116A-B may be included within, or part of, the dataprocessing system 120; accordingly, the digitized signals may be passedto other components of the data processing system 120.

In some instances, the data acquisition system 116A-B may include theability to receive the series of images 112 from the one or more cameras108A-B. The series of images 112, already having been digitized, may, insome instances, be conditioned via the signal conditioning module 336and may then be provided to the data processing system 120 via thecommunication interface 356. In other instances, the series of images112 from the one or more cameras 108A-B may be provided directly to thedata processing system 120. It should be understood that a series ofimages 112 refers to one or more images from one or more cameras 108A-B.That is, in accordance with embodiments of the present disclosure, aseries of images 112 may be acquired by one or more cameras 108A-B andmay be provided as a series or individually to the data processingsystem 120. In an embodiment, an “image” may include a full image. Inanother embodiment, an “image” may include a portion of an image, asegment of a full image, a thumbnail of an image, and/or an icon thatpertains to an image. Another embodiment of an “image” may include aphotograph and/or a digital image that can be captured by an imagecapture device, such as, for example, the one or more cameras 108A-B.Certain embodiments of a streaming image may include a video that may becaptured by the one or more cameras 108A-B, and the streaming image, orimages, may be provided to the data acquisition system 116A-B and/or thedata processing system 120.

FIG. 4 provides a schematic overview of an example correlation processfor one or more pixels in accordance with embodiments of the presentdisclosure. A series of images 404, 408, and 412, corresponding to aparticular point in time t, t+1, and t+2, respectively, depict adisplacement of area of interest 416. The series of images 404, 408, and412, may be acquired by the one or more cameras 108A-B and each of whichmay correspond to an entire image in the series of images 112 or maycorrespond to a portion of an image in the series of images 112. Such aseries of images 404, 408, and 412 may also correspond to a specificframe in a stream of images.

Alternatively, or in addition, images 404, 408, and 412 may correspondto a synthesized image of a three-dimensional contour based on an imageacquired by camera 108A and an image acquired by camera 108B.Alternatively, or in addition, images 404, 408, and 412 may correspondto a synthesized image of a three-dimensional contour based on an imageacquired by camera 108A, an image acquired by camera 108B, and a thirdcamera. Accordingly, although the images 404, 408, and 412 depict atwo-dimensional correlation process, which may be achieved using asingle camera 108, techniques described herein are applicable to athree-dimensional correlation process as well. That is, forthree-dimensional measurements, at least two cameras are utilized.Assuming the positions of the at least two cameras relative to eachother are known, as well as magnifications of the lenses and otherimaging parameters, the absolute three-dimensional coordinates of anysurface point of a component of interest 124 may be calculated resultingin a three-dimensional surface contour. Therefore, the pixel ofinterest, or area of interest, may be tracked through each image 404,408, and 412 in three dimensions.

Referring again to FIG. 4, at a time equal to t, the image 404 isacquired. Within image 404, a particular pixel of interest 420 may bespecified, of which a box, or correlation square 416 around the pixel ofinterest 420 may be specified. In some instances, the pixel of interest420 may be two-dimensionally and/or three-dimensionally tracked fromimage to image (i.e., 404, 408, and 412) where a displacement signalcorresponding to the movement of the pixel of interest 420 within theimages 404, 408, and 412 is recorded. Alternatively, or in addition, thecorrelation square 416 may be two-dimensionally and/orthree-dimensionally tracked from image to image (i.e., 404, 408, and412), where a displacement signal corresponding to the movement of thecorrelation square 416 within the images is recorded and is provided asa displacement signal. That is, as illustrated in FIG. 4, the absoluteand/or relative displacement of the pixel of interest 420 and/or thecorrelation square 416 between image 404 at a time equal to t and image408 at a time equal to t+1 may be calculated. For example, thedisplacement of the pixel of interest 420 and/or the correlation square416 between image 404 at a time equal to t and image 408 at a time equalto t+1 is one pixel in the x direction and one pixel in the negative ydirection. Similarly, the displacement of the pixel of interest 420and/or the correlation square 416 between image 408 at a time equal tot+1 and image 412 at a time equal to t+2 is one pixel in the x directionand no pixels in the y direction. Of course, if the pixel of interest420 and/or the correlation square 416 were tracked three-dimensionally,a displacement in the z direction may be obtained. In some instances,the pixel of interest 420 and/or the correlation square 416 maycorrespond to the speckle pattern 136 and/or tracking pattern/mark 140of the component of interest 124, the non-filtered sensor 144, and/orthe analog low-pass filtered sensor 148.

As a simplified example, a normalized cross-correlation process may beutilized to correlate a part of the source image, such as a correlationsquare 416 within the image 404, to a target image, such as image 408.In some embodiments, the grey scale levels of the pixels in thecorrelation square 416 within the source image may be compared to all ofthe pixels, or a localized subset of pixels, within the target image408. Of all the locations searched, a location in the target image thatcorresponds best to the correlation square 416 may be based on thehighest correlation value. A resulting displacement, such asdisplacement in x and y directions as illustrated in FIG. 4, may then begenerated. Of course, other methods and digital correlation processesmay be utilized resulting in higher accuracy. For example, a gridpattern with multiple rows and columns of grid points in the sourceimage may be matched or otherwise correlated to an area in the targetimage. A resulting displacement of the grid points may then be utilizedto generate a displacement signal. The displacement signal may be for asingle direction or axis, such as in the x direction, or a combinationof directions. For example, a displacement vector, indicative of anamount of displacement from an origin point in the x, y, and zdirections may be generated. FIG. 5 generally illustrates an example ofa displacement signal in accordance with embodiments of the presentdisclosure. That is, a raw unfiltered digital image correlation signalis illustrated over time. As previously discussed, such anoptically-derived displacement signal may correspond to a displacementsignal for the component of interest 124, the region of interest 132,the non-filtered sensor 144, the analog low-pass filtered sensor 148,and/or another location or component within the region of interest 132.

Referring now to FIG. 6, a method 600 for realizing the practicalapplications of optically-generated dynamic quantities of interest withalias protection based on a series of images will be discussed inaccordance with embodiments of the present disclosure. Method 600 is inembodiments performed by one or more devices, such as the one or moredevices included in the system 100. More specifically, one or morehardware and software components including the data processing system120 in conjunction with information provided by the one or more cameras108A-B, a data acquisition system 116A-B, a non-filtered sensor 144,and/or an analog low-pass filtered sensor 148 may be involved inperforming method 600. In one embodiment, one or more of the previouslydescribed modules and/or devices perform one or more of the steps ofmethod 600. The method 600 may be executed as a set ofcomputer-executable instructions, executed by a data processing system120 in conjunction with information provided from the one or morecameras 108A-B, a data acquisition system 116A-B, a non-filtered sensor144, and/or an analog low-pass filtered sensor 148, encoded or stored ona computer-readable medium. Hereinafter, the method 600 shall beexplained with reference to systems, components, modules, software, etc.described with reference to FIGS. 1-5.

Method 600 may continuously flow in a loop, flow according to a timedevent, or flow according to a change in an operating or statusparameter. Method 600 is initiated at step S604 where a test of thecomponent of interest 124 using a shock source device 128 is initiated.At step S608, a series of images 112 is acquired by the one or morecameras 108A-B and is provided to the data acquisition system 116A-Band/or the data processing system 120. As previously discussed, theseries of images 112 includes the non-filtered sensor 144 and the analoglow-pass filtered sensor 148 within the region of interest 132.Alternatively, or in addition, one or more of the sensors 144 and 148may be located outside the region of interest 132. Simultaneously withthe acquisition of the series of images 112, sensor data from anon-filtered sensor 144 and an analog low-pass filtered sensor 148 isacquired. Such sensor data may be provided to the data acquisitionsystem 116A-B and/or the data processing system 120. Further thenon-filtered sensor 144 and the analog low-pass filtered sensor 148 maybe the same type of sensor, where the signal from the analog low-passfiltered sensor 148 is subject to low-pass filtering at the dataacquisition system 116A-B. Alternatively, or in addition, the analoglow-pass filtered sensor 148 may include a low-pass filter.

At step S612, a displacement signal for each of the non-filtered sensor144 and the analog low-pass filtered sensor 148 is generated based onone or more optical tracking techniques. As previously discussed, one ormore optical tracking techniques, such as digital image correlation, maybe used to track the surface displacement and/or deformation of thenon-filtered sensor 144 and generate a displacement signalrepresentative of such based on the series of images 112 and themovement of the speckle pattern 136 and/or the tracking pattern/mark140. Similarly, one or more optical tracking techniques, such as digitalimage correlation, may be used to track the surface displacement and/ordeformation of the analog low-pass filtered sensor 148 and generate adisplacement signal representative of such based on the series of images112 and the movement of the speckle pattern 136 and/or the trackingpattern/mark 140.

At step S616, the optically-generated displacement signal based on theseries of images 112 for the non-filtered sensor 144 and the analoglow-pass filtered sensor 148 may be filtered. As previously discussed, awavelet filter may be used to filter each of the generated displacementsignals. The wavelet filter type and decomposition level may bepredetermined and/or preselected based on, for example, a test type, adisplacement signal range, a perceived noise measurement, an actualnoise measurement, the result of step S628, and/or a combinationthereof. Method 600 may then proceed to step S620 where anoptically-derived dynamic quantity of interest is generated for thenon-filtered sensor 144 and/or the analog low-pass filtered sensor 148based on the respective optically-tracked displacement signals for thenon-filtered sensor 144 and/or the analog low-pass filtered sensor 148.

At step S624, a sensor derived dynamic quantity of interest iscalculated for each of the non-filtered sensor 144 and the analoglow-pass filtered sensor 148. That is, based on sensor data providedfrom the sensor, a dynamic quantity of interest is calculated.Accordingly, at step S628, the generated optically-derived dynamicquantity of interest for each of the sensors is compared to theassociated sensor-derived calculation of the dynamic quantity ofinterest. For example, at step S628 an indication of how well theoptically-derived dynamic quantity of interest matches thesensor-derived calculation of the dynamic quantity of interest may begenerated. Such an indication may be based on a similarity algorithm,such as the sum of the absolute differences between theoptically-derived dynamic quantity of interest and the sensor-derivedcalculation of the dynamic quantity of interest for each sensor. Othersimilarity algorithms, such as, but not limited to, the sum of thesquared differences and variance, are also contemplated. Based on thecomparison score, for example, the filter may be adjusted at step S632.That is, if the similarity comparison indicates that the sum of thesquared differences is high, a different wavelet filter decompositionlevel and/or a different wavelet filter altogether may be selected.Accordingly, steps S616 to S632 may repeat until the similarity score isbelow a predetermined and/or preselected threshold. Alternatively, or inaddition, a predetermined number of wavelet filters and/or apredetermined number of wavelet decomposition levels for each waveletfilter may be evaluated at steps S616 to S632, where the wavelet filterand wavelet decomposition level yielding a highest similarity, forexample the lowest SSD value, may be chosen as the final wavelet filterand the final wavelet filter decomposition level.

The existence of aliasing and/or the determination as to whetheraliasing is likely to be present may occur at step S636. At step S636,the sensor data from the non-filtered sensor 144 and the analog low-passfiltered sensor 148 may be compared. Such a comparison may rely on rawsensor data and/or other derived measures based on the raw sensor data.For example, a similarity algorithm, as previously discussed, may beused to determine a similar score, such as a sum of squared differences,between the raw sensor data for the non-filtered sensor 144 and the rawsensor data for the analog low-pass filtered sensor 148. Alternatively,or in addition, the power spectral densities for both the non-filteredsensor 144 and the analog low-pass filtered sensor 148 may be evaluated.Alternatively, or in addition, the presence of aliasing may bedetermined based on a comparison, or similarity, between each of thesensor-derived calculations of the dynamic quantity of interest. As onenon-limiting example, when evaluating the shock response spectrum foreach of the non-filtered sensor 144 and the analog low-pass filteredsensor 148, the SRS for the non-filtered sensor 144 may be higher atsome or all frequencies than the SRS for the analog low-pass filteredsensor 148. Such an offset, or deviation, may indicate that aliasing isor is likely to be present.

Alternatively, or in addition, the determination as to whether aliasingis or is likely to be present may be based at least partially on whetherthe optically-derived dynamic quantity of interest for each sensorlocation is more similar to the sensor derived dynamic quantity ofinterest for the non-filtered sensor 144 or the sensor derived dynamicquantity of interest for the analog low-pass filtered sensor 148. Thatis, if the similarity is greater between the optically-derived dynamicquantity of interest for the non-filtered sensor 144 and thesensor-derived dynamic quantity of interest for the non-filtered sensor144 than the similarity between the optically-derived dynamic quantityof interest for the analog low-pass filtered sensor 148 and thesensor-derived dynamic quantity of interest for the analog low-passfiltered sensor 148, aliasing may exist or otherwise may be influencingthe optically-derived dynamic quantity of interest. For example,aliasing may cause the optically-derived dynamic quantity of interest tobe slightly higher than or otherwise offset from the sensor-deriveddynamic quantity of interest resulting in such a lower similarity.Accordingly, as a result of step S632, an indication as to whetheraliasing is or is likely to be present may be generated based on suchcomparisons.

For example, if aliasing is likely to be present, the optically-deriveddynamic quantity of interest may be corrected, tagged, or indicated aspotentially erroneous at optionally identified step S640. Alternatively,or in addition, an optically-derived displacement signal for thecomponent of interest 124 may be generated at step S644. Based on thedetermination of a wavelet filter type and decomposition level by any ofsteps S616 to S632, the optically-derived displacement signal for thecomponent of interest 124 may be wavelet filtered at step S648. That is,the wavelet filter type and level may be applied to otheroptically-derived displacement signals based on the series of images112. As previously indicated, the determined wavelet filter type anddecomposition level at either of steps S616-S632 may be utilized towavelet filter an optically-derived displacement signal for an objectother than the non-filtered sensor 144 and/or the analog low-passfiltered sensor 148. Such object may be the component of interest 124and/or the region of interest 132. Accordingly, dynamic quantities ofinterest based on the wavelet filtered optically-derived displacementsignal for the component of interest 124 may be generated at step S652.Method 600 may then end at step S656.

Referring now to FIG. 7, a method 700 for realizing the practicalapplications of digital image correlation derived dynamic quantities ofinterest with alias protection based on a series of images will bediscussed in accordance with embodiments of the present disclosure.Method 700 is in embodiments, performed by one or more devices, such asthe one or more devices included in the system 100. More specifically,one or more hardware and software components including the dataprocessing system 120 in conjunction with information provided by theone or more cameras 108A-B, a data acquisition system 116A-B, anon-filtered sensor 144, and/or an analog low-pass filtered sensor 148may be involved in performing method 700. In one embodiment, one or moreof the previously described modules and/or devices perform one or moreof the steps of method 700. The method 700 may be executed as a set ofcomputer-executable instructions, executed by a data processing system120 in conjunction with information provided from the one or morecameras 108A-B, a data acquisition system 116A-B, a non-filtered sensor144, and/or an analog low-pass filtered sensor 148, encoded or stored ona computer-readable medium. Hereinafter, the method 700 shall beexplained with reference to systems, components, modules, software, etc.described with FIGS. 1-6.

Method 700 may continuously flow in a loop, flow according to a timedevent, or flow according to a change in an operating or statusparameter. Method 700 is initiated at step S704 where a test ofcomponent of interest 124 using a shock source device 128 is initiated.At step S708, a series of images 112 is acquired by the one or morecameras 108A-B and provided to the data acquisition system 116A-B and/orthe data processing system 120. As previously discussed, the series ofimages 112 includes the non-filtered sensor 144, such as a non-filteredaccelerometer, and the analog low-pass filtered sensor 148, such as ananalog low-pass filtered accelerometer, within the region of interest132. Simultaneously with the acquisition of the series of images 112,accelerometer data from a non-filtered sensor 144 and an analog low-passfiltered sensor 148 is acquired. Such accelerometer data may be providedto the data acquisition system 116A-B and/or the data processing system120. Further, the non-filtered sensor 144 and the analog low-passfiltered sensor 148 may be the same type of sensor, where theacceleration signal from the analog low-pass filtered sensor 148 issubject to low-pass filtering at the data acquisition system 116A-B.Alternatively, or in addition, the analog low-pass filtered sensor 148may include a low-pass filter.

At step S712, a digital image correlation displacement signal for theanalog low-pass filtered sensor 148 is generated in accordance with atleast some of the previously discussed digital image correlationprocessing techniques. That is, DIC processing techniques may beutilized to generate a displacement signal based on the surfacedisplacement and/or deformation of the analog low-pass filtered sensor148. At step S716, the DIC-derived displacement signal based on theseries of images 112 for the analog low-pass filtered sensor 148 may befiltered. As previously discussed, a wavelet filter may be used tofilter the DIC-derived displacement signal to reduce and/or eliminatenoise associated with the DIC displacement signal. The wavelet filtertype and decomposition level may be predetermined and/or preselectedbased on, for example, a test type, a displacement signal range, aperceived noise measurement, an actual noise measurement, the result ofstep S732, and/or a combination thereof. Method 700 may then proceed tostep S720 where a DIC-derived dynamic quantity of interest signal isgenerated based on the DIC-derived displacement signal for the analoglow-pass filtered sensor 148. For example, a center differencedifferentiation may be utilized to calculate a first derivative of theDIC-derived displacement signal for the analog low-pass filtered sensor148; such a calculation may be used to generate a DIC-derived velocitybased on the DIC-derived displacement signal for the analog low-passfiltered sensor 148. Accordingly, a derivative of the DIC-derivedvelocity signal may be obtained; such a derivative may be used to obtaina DIC-derived acceleration signal. When determining DIC-derived dynamicquantity of interest data, for example a Shock Response Spectrumcalculation, the DIC-derived acceleration signal may be used to generatesuch dynamic quantity of interest data.

Similarly, at step S724, a sensor-derived dynamic quantity of interestsignal is generated based on one or more signals received from theanalog low-pass filtered sensor 148. For example, to determinesensor-derived dynamic quantity of interest data, for example a ShockResponse Spectrum calculation, an acceleration signal from the analoglow-pass filtered sensor 148 may be used to generate such dynamicquantity of interest data. At step S728, the DIC-derived dynamicquantity of interest data for the analog low-pass filtered sensor 148may be compared to the associated sensor-derived dynamic quantity ofinterest data. For example, at step S728, an indication of how well theDIC-derived dynamic quantity of interest matches the sensor-deriveddynamic quantity of interest may be generated. Such an indication may bebased on a similarity algorithm, such as the sum of the absolutedifferences between the DIC-derived dynamic quantity of interest and thesensor-derived dynamic quantity of interest for the analog low-passfiltered sensor 148. Other similarity algorithms, such as, but notlimited to, the sum of the squared differences and variance, are alsocontemplated. Based on the comparison score, for example, at step S732,the filter may be adjusted. That is, if the similarity comparisonindicates that the sum of the squared differences is high, a differentwavelet filter decomposition level and/or a different wavelet filteraltogether may be selected. Accordingly, steps S716 to S732 may repeatuntil the similarity score is below a predetermined and/or preselectedthreshold. Alternatively, or in addition, a predetermined number ofwavelet filters and/or a predetermined number of wavelet decompositionlevels for each wavelet filter may be evaluated at steps S716 to S732,where the wavelet filter and wavelet decomposition level yielding ahighest similarity, for example the lowest SSD value, may be chosen asthe final wavelet filter and the final wavelet filter decompositionlevel.

Steps S736 to S756 may be performed in parallel and/or may sequentiallyfollow step S732. That is, the filter applied to the DIC-derived dynamicquantity of interest based on the non-filtered sensor 144 may bedifferent from, or in some cases, the same as, the filter applied to theDIC-derived dynamic quantity of interest based on the analog low-passfiltered sensor 148. Accordingly, at step S736, the DIC-deriveddisplacement signal based on the series of images 112 for thenon-filtered sensor 144 may be filtered. As previously discussed, awavelet filter may be used to filter the DIC-derived displacement signalto reduce and/or eliminate noise associated with the DIC displacementsignal. The wavelet filter type and decomposition level may bepredetermined and/or preselected based on, for example, a test type, adisplacement signal range, a perceived noise measurement, an actualnoise measurement, the result of step S756, and/or a combinationthereof. Method 700 may then proceed to step S740 where a DIC-deriveddynamic quantity of interest signal is generated based on theDIC-derived displacement signal for the non-filtered sensor 144. Forexample, a center difference differentiation may be utilized tocalculate a first derivative of the DIC-derived displacement signal forthe non-filtered sensor 144; such a calculation may be used to generatea DIC-derived velocity based on the DIC-derived displacement signal forthe non-filtered sensor 144. Accordingly, a derivative of theDIC-derived velocity signal may be obtained; such a derivative may beused to obtain a DIC-derived acceleration signal based on the DICdisplacement signal of the non-filtered sensor 144. When determiningDIC-derived dynamic quantity of interest data, for example a ShockResponse Spectrum calculation, the DIC-derived acceleration signal maybe used to generate such dynamic quantity of interest data.

Similarly, at step S748, a sensor-derived dynamic quantity of interestsignal is generated based on one or more signals received from thenon-filtered sensor 144. For example, to determine sensor deriveddynamic quantity of interest data, for example a Shock Response Spectrumcalculation, an acceleration signal from the non-filtered sensor 144 maybe used to generate such dynamic quantity of interest data. At stepS752, the DIC-derived dynamic quantity of interest data for thenon-filtered sensor 144 may be compared to the associated sensor-deriveddynamic quantity of interest data. For example, at step S752, anindication of how well the DIC-derived dynamic quantity of interestmatches the sensor-derived dynamic quantity of interest may begenerated. Such an indication may be based on a similarity algorithm,such as the sum of the absolute differences between the DIC-deriveddynamic quantity of interest and the sensor-derived dynamic quantity ofinterest for the non-filtered sensor 144. Other similarity algorithms,such as, but not limited to, the sum of the squared differences andvariance, are also contemplated. Based on the comparison score, forexample, at step S756, the filter may be adjusted. That is, if thesimilarity comparison indicates that the sum of the squared differencesis high, a different wavelet filter decomposition level and/or adifferent wavelet filter altogether may be selected. Accordingly, stepsS740 to S756 may repeat until the similarity score is below apredetermined and/or preselected threshold. Alternatively, or inaddition, a predetermined number of wavelet filters and/or apredetermined number of wavelet decomposition levels for each waveletfilter may be evaluated at steps S740 to S756, where the wavelet filterand wavelet decomposition level yielding a highest similarity, forexample the lowest SSD value, may be chosen as the final wavelet filterand the final wavelet filter decomposition level.

The existence of aliasing and/or the determination as to whetheraliasing is likely to be present may occur at step S760. At step S760,the sensor data from the non-filtered sensor 144 and the analog low-passfiltered sensor 148 may be compared. Such a comparison may rely on rawsensor data and/or other derived measures based on the raw accelerationdata. For example, a similarity algorithm, as previously discussed, maybe used to determine a similar score, such as a sum of squareddifferences, between the raw sensor data for the non-filtered sensor 144and the raw sensor data for the analog low-pass filtered sensor 148.Alternatively, or in addition, the power spectral densities for both thenon-filtered sensor 144 and the analog low-pass filtered sensor 148 maybe evaluated. Alternatively, or in addition, the presence of aliasingmay be determined based on a comparison, or similarity, between each ofthe sensor-derived calculations of the dynamic quantity of interest. Asone non-limiting example, when evaluating the shock response spectrumfor each of the non-filtered sensor 144 and the analog low-pass filteredsensor 148, the SRS for the non-filtered sensor 144 may be higher atsome or all frequencies than the SRS for the analog low-pass filteredsensor 148. Such an offset, or deviation, may indicate that aliasing isor is likely to be present. Accordingly, at step S764, an indicationindicating aliasing is or is likely to be present is generated. Such anindication may be rendered to, or otherwise presented to a display, suchas the user output 216.

Alternatively, or in addition, the determination as to whether aliasingis or is likely to be present may be based at least partially on whetherthe DIC-derived dynamic quantity of interest data for each sensorlocation is more similar to the sensor-derived dynamic quantity ofinterest for the non-filtered sensor 144 or the sensor-derived dynamicquantity of interest for the analog low-pass filtered sensor 148. Thatis, if the similarity is greater between the DIC-derived dynamicquantity of interest for the non-filtered sensor 144 and thesensor-derived dynamic quantity of interest for the non-filtered sensor144 than the similarity between the DIC-derived dynamic quantity ofinterest for the analog low-pass filtered sensor 148 and thesensor-derived dynamic quantity of interest for the analog low-passfiltered sensor 148, aliasing may exist or otherwise may be influencingthe DIC-derived dynamic quantity of interest. For example, aliasing maycause the DIC-derived dynamic quantity of interest to be slightly higherthan or otherwise offset from the sensor derived dynamic quantity ofinterest; such offset may result in such a lower similarity.Accordingly, as a result of step S760, an indication as to whetheraliasing is or is likely to be present may be generated based on suchcomparisons at step S764. Such an indication may be rendered to orotherwise presented on a display, such as the user output 216.

In accordance with some embodiments of the present disclosure, asimilarity between the DIC-derived dynamic quantity of interest and thesensor-derived dynamic quantity of interest may be range dependent. Thatis, as illustrated in at least FIG. 12, above and/or below certainranges, the DIC-derived dynamic quantity of interest may be more or lesssimilar to the sensor-derived dynamic quantity of interest. Accordingly,such ranges of similarity may be further identified. Method 700 may thenend at step S768. As a result of method 700 for example, if aliasing islikely to be present, the DIC dynamic quantity of interest may becorrected, tagged, or indicated as potentially erroneous at optionallyidentified step S768. Alternatively, or in addition, a DIC-deriveddisplacement signal for the component of interest 124 may be generatedat step S772. Based on the determination of a wavelet filter type anddecomposition level by any of steps S716 to S732 and steps S740 to S756,the DIC-derived displacement signal for the component of interest 124may be wavelet filtered at step S776. That is, the wavelet filter typeand level determined by either of steps S716 to S732 and steps S740 toS756 may be applied to other DIC-derived displacement signals based onthe series of images 112. Such DIC-derived displacement signals may befor the component of interest 124 and/or other components within theregion of interest 132. Accordingly, dynamic quantities of interestbased on the wavelet filtered DIC-derived displacement signal for thecomponent of interest 124 may be generated at step S780. Method 700 maythen end at step S784.

Referring now to FIG. 8, an example of a DIC-derived displacement signaland the resulting filtered DIC-derived displacement signal in accordancewith embodiments of the present disclosure is illustrated. That is, araw unfiltered digital image correlation signal is illustrated in thetime domain; such a signal is the same as or similar to the signalillustrated in FIG. 5. Such a DIC-derived displacement signal maycorrespond to a displacement signal for the component of interest 124,the non-filtered sensor 144, the analog low-pass filtered sensor 148,another location or component within the region of interest 132, and/orthe region of interest 132 itself. Accordingly, as a result of a waveletfiltering process, for example as a result of steps S616, S716, and/orS740, a wavelet filtered DIC displacement signal may be obtained asillustrated by the solid line.

Referring now to FIG. 9, an example of a DIC-derived dynamic quantity ofinterest is illustrated in accordance with embodiments of the presentdisclosure. That is, a shock response spectrum is depicted illustratingan SRS dynamic quantity of interest based on the RAW DIC, thatis—unfiltered DIC-derived displacement signal. FIG. 9 illustrates apotential problem with generating dynamic quantities of interest basedon unfiltered DIC-derived displacement signals. That is, the DIC-derivedSRS signal is higher than the sensor-derived SRS signal. Accordingly, asa result of proper filtering, such as wavelet filtering at steps S616,S716, and/or S740, the noise associated with the DIC-deriveddisplacement signal may be reduced or otherwise eliminated. Accordingly,subsequent derivatives of such a signal are not amplifying the noiseotherwise removed. Therefore, as illustrated by at least FIG. 9, the SRSsignal based on wavelet filtered DIC data more closely matches thesensor-derived SRS signal.

In accordance with some embodiments of the present disclosure, whenundertaking some shock-related testing, sensors may exhibit signs ofsaturation. That is, as illustrated in at least FIG. 10, a bottomaccelerometer signal illustrates clear signs of saturation. Saturationgenerally occurs when a sensor's natural resonant frequency is excitedand often results in artificially high spectral content around one ormore frequency ranges. Accordingly, the effects of saturation can beremoved and corrected utilizing one or more processes, as illustrated bya top accelerometer signal in FIG. 10. However, as such signal iscorrected, dynamic quantities of interest derived from the sensor signalmay be affected as will be discussed with respect to FIG. 12.

Referring to FIG. 11, an example of a DIC-derived displacement signaland the resulting filtered DIC-derived displacement signal in accordancewith embodiments of the present disclosure is illustrated. That is, araw unfiltered digital image correlation signal based on the correctedaccelerometer saturation signal of FIG. 10 is illustrated in the timedomain. Such a DIC-derived displacement signal may correspond to adisplacement signal for the component of interest 124, the non-filteredsensor 144, the analog low-pass filtered sensor 148, another location orcomponent within the region of interest 132, and/or the region ofinterest 132 itself; however, in accordance with embodiments of thepresent disclosure, the displacement signal is associated with at leastone of the non-filtered sensor 144 or analog low-pass filtered sensor148. As a result of a wavelet filtering process, for example, as aresult of steps S616, S716, and/or S740, a wavelet filtered DICdisplacement signal may be obtained as illustrated by the solid line.

Referring now to FIG. 12, an example of a DIC-derived dynamic quantityof interest based on the DIC-derived displacement signal of FIG. 11 isillustrated in accordance with embodiments of the present disclosure.That is, a shock response spectrum is depicted illustrating an SRSdynamic quantity of interest based on the raw unfiltered DIC-deriveddisplacement signal. FIG. 12 illustrates that at certain frequencies,the SRS signal based on the wavelet filtered DIC-derived displacementsignal may match, or otherwise be similar to, a SRS signal derived froman accelerometer. Similar to FIG. 9, however, the SRS signal based onraw unfiltered DIC-derived data of FIG. 12 may be higher than anaccelerometer-derived SRS signal. As a result of proper filtering, suchas wavelet filtering at steps S616, S716, and/or S740, the noiseassociated with the DIC-derived displacement signal may be reduced orotherwise eliminated and the DIC-derived SRS signal may more accuratelyrepresent or otherwise be more similar to the accelerometer-derived SRSsignal. However, as in the case of FIG. 12, the accelerometer has beensaturated (see. e.g., FIG. 10); therefore, the SRS signal based on awavelet filtered and DIC-derived displacement signal may not match theaccelerometer-derived SRS signal in all frequencies. However, since theSRS signal based on the wavelet filtered DIC-derived displacement signalnear matches the accelerometer-derived displacement signal above 1000Hz, the exclusion of the accelerometer derived SRS signal may bewarranted below 1000 Hz. Accordingly, the SRS signal based on thewavelet filtered DIC-derived displacement signal may more accuratelyrepresent the accelerometer-derived SRS signal for some frequencyranges.

While the non-filtered sensor 144 and the analog low-pass filteredsensor 148 have been illustrated as not being directly attached to thecomponent of interest 124, in some embodiments, such sensors may bemounted directly to the component of interest 124. In such embodiments,the sensors tend to be of similar size and weight to the component ofinterest. Further, such an embodiment provides for a more direct sourceshock measurement, particularly on curved components of interest, orcomponents of interest mounted to a curved surface or object.Accordingly, dynamic quantities of interest may be generated for curvedsurfaces without adding additional hardware and without furthercomponent modifications, such as drilling/tapping the component.Moreover, such embodiments may provide the ability to measure high “G”environments without damaging sensors or modifying the component toensure test sensors stay in contact with the component or sacrificinglow frequency response. Similarly, embodiments in accordance with thepresent disclosure provide the ability to perform spatial measurements,measure more locations and directions per test, and further measurenon-traditional dynamic quantities of interest, such as, but not limitedto, strains, surface plasticity, and power.

In the foregoing description, for the purposes of illustration, methodswere described in a particular order. It should be appreciated that inalternate embodiments, the methods may be performed in a different orderthan that described. It should also be appreciated that the methodsdescribed above may be performed by hardware components or may beembodied in sequences of machine-executable instructions, which may beused to cause a machine, such as a general-purpose or special-purposeprocessor or logic circuits programmed with the instructions to performthe methods. These machine-executable instructions may be stored on oneor more machine readable mediums, such as CD-ROMs or other type ofoptical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magneticor optical cards, flash memory, or other types of machine-readablemediums suitable for storing electronic instructions. Alternatively, themethods may be performed by a combination of hardware and software.

Also, it is noted that the embodiments were described as a process whichis depicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart may describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be re-arranged. A process is terminated when itsoperations are completed, but could have additional steps not includedin the figure. A process may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, hardware description languages, or anycombination thereof. When implemented in software, firmware, middlewareor microcode, the program code or code segments to perform the necessarytasks may be stored in a machine readable medium such as storage medium.A processor(s) may perform the necessary tasks. A code segment mayrepresent a procedure, a function, a subprogram, a program, a routine, asubroutine, a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

Specific details were given in the description to provide a thoroughunderstanding of the embodiments. However, it will be understood by oneof ordinary skill in the art that the embodiments may be practicedwithout these specific details. For example, circuits may be shown inblock diagrams in order not to obscure the embodiments in unnecessarydetail. In other instances, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments. While illustrative embodimentsof the invention have been described in detail herein, it is to beunderstood that the inventive concepts may be otherwise variouslyembodied and employed, and that the appended claims are intended to beconstrued to include such variations, except as limited by the priorart.

What is claimed is:
 1. A system comprising: a data acquisition deviceconfigured to capture data associated with a component within at leastone region of interest; a sensor; and a data processing system adaptedto receive data associated with the sensor and data captured by the dataacquisition device, the data processing system including: at least oneprocessor; and memory storing one or more program instructions that whenexecuted by the at least one processor, execute the steps of: generatinga first dynamic quantity of interest for the component within the atleast one region of interest, comparing the first dynamic quantity ofinterest for the component to a second dynamic quantity of interestbased on the data associated with the sensor, and filtering dataassociated with a third dynamic quantity of interest based on a measureof similarity between the first dynamic quantity of interest for thecomponent and the second dynamic quantity of interest based on the dataassociated with the sensor.
 2. The system of claim 1, wherein the firstdynamic quantity of interest is based on a non-contact type measurement.3. The system of claim 2, wherein the sensor is an accelerometer.
 4. Thesystem of claim 2, wherein the at least one region of interest includesthe sensor.
 5. The system of claim 2, wherein the data acquisitiondevice is configured to capture one or more images and the one or moreimages include the at least one region of interest.
 6. The system ofclaim 5, wherein the first dynamic quantity of interest for thecomponent is an optically-derived dynamic quantity of interest and isgenerated from the one or more images using at least one digital imagecorrelation technique.
 7. The system of claim 1, further comprising:generating an optically-derived dynamic quantity of interest for asecond component within the at least one region of interest; andfiltering data associated with the optically-derived dynamic quantity ofinterest for the second component.
 8. The system of claim 1, wherein thesensor data associated with the sensor is analog low-pass filteredsensor data.
 9. A method comprising: generating a first dynamic quantityof interest based on a non-contact type measurement for a componentwithin at least one region of interest, comparing the first dynamicquantity of interest for the component to a second dynamic quantity ofinterest based on data associated with a sensor, and filtering dataassociated with at least one dynamic quantity of interest based on ameasure of similarity between the first dynamic quantity of interest forthe component and the second dynamic quantity of interest based on thedata associated with the sensor.
 10. The method of claim 9, wherein thesensor is an accelerometer.
 11. The method of claim 9, wherein the atleast one region of interest includes the sensor.
 12. The method ofclaim 9, further comprising: generating the optically-derived dynamicquantity of interest for the component from one or more images using atleast one digital image correlation technique.
 13. The method of claim12, wherein the one or more images include the at least one region ofinterest.
 14. The method of claim 1, further comprising: generating anoptically-derived dynamic quantity of interest for a second componentwithin the at least one region of interest; and filtering dataassociated with the optically-derived dynamic quantity of interest forthe second component based on the measure of similarity between thefirst dynamic quantity of interest for the component and the seconddynamic quantity of interest based on the data associated with thesensor.
 15. A method comprising: generating a first dynamic quantity ofinterest based on data associated with a first sensor; generating asecond dynamic quantity of interest based on data associated with asecond sensor; generating third dynamic quantity of interest based on anon-contact type measurement for a component within at least one regionof interest, comparing the first dynamic quantity of interest to thesecond dynamic quantity of interest, and filtering data associated withthe third dynamic quantity of interest for the component within the atleast one region of interest based on a measure of similarity betweenthe first dynamic quantity of interest and the second dynamic quantityof interest.
 16. The method of claim 15, wherein the first sensor is anaccelerometer and the second sensor is an accelerometer.
 17. The methodof claim 15, wherein the at least one region of interest includes thefirst sensor.
 18. The method of claim 15, further comprising: generatingthe third dynamic quantity of interest for the component from one ormore images using at least one digital image correlation technique. 19.The method of claim 15, wherein the data associated with the firstsensor is analog low-pass filtered data and the data associated with thesecond sensor is not analog low-pass filtered data.
 20. The method ofclaim 15, further comprising: generating an optically-derived dynamicquantity of interest for a second component within the at least oneregion of interest; and filtering data associated with theoptically-derived dynamic quantity of interest for the second componentbased on the measure of similarity between the first dynamic quantity ofinterest and the second dynamic quantity of interest.